

'''simple docstring'''
from collections import Counter
from timeit import timeit




def 	UpperCamelCase   (       lowercase_						:    str = ""     ,					)		->	bool:




  '''simple docstring'''



  return sum(c % 2 for c in Counter(input_str.replace(''' '''     ,					''''''	).lower()	).values()	) < 2




def 	UpperCamelCase   (       lowercase_						:    str = ""	)		->	bool:




  '''simple docstring'''



  if len(lowercase_	) == 0:
    return True
  lowercase									=input_str.replace(''' '''     ,					''''''	).lower()
  # character_freq_dict: Stores the frequency of every character in the input string
  lowercase									={}

  for character in lower_case_input_str:
    lowercase									=character_freq_dict.get(lowercase_     ,					0	) + 1
  lowercase									=0

  for character_count in character_freq_dict.values():
    if character_count % 2:
      odd_char += 1
  if odd_char > 1:
    return False
  return True




def 	UpperCamelCase   (       lowercase_						:    str = ""	)		->	None:




  '''simple docstring'''



  print('''\nFor string = '''     ,					lowercase_     ,					''':'''	)
  print(
      '''> can_string_be_rearranged_as_palindrome_counter()'''     ,					'''\tans ='''     ,					can_string_be_rearranged_as_palindrome_counter(lowercase_	)     ,					'''\ttime ='''     ,					timeit(
          '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''     ,					setup='''import __main__ as z'''     ,					)     ,					'''seconds'''     ,					)
  print(
      '''> can_string_be_rearranged_as_palindrome()'''     ,					'''\tans ='''     ,					can_string_be_rearranged_as_palindrome(lowercase_	)     ,					'''\ttime ='''     ,					timeit(
          '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''     ,					setup='''import __main__ as z'''     ,					)     ,					'''seconds'''     ,					)


if __name__ == "__main__":
     _UpperCAmelCase  :		List[str]  =		input(
         '''Enter string to determine if it can be rearranged as a palindrome or not: '''
     ).strip()
     benchmark(check_str)
     _UpperCAmelCase  :		str  =		can_string_be_rearranged_as_palindrome_counter(check_str)
     print(F"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase					={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["MobileBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileBertForMaskedLM",
      "MobileBertForMultipleChoice",
      "MobileBertForNextSentencePrediction",
      "MobileBertForPreTraining",
      "MobileBertForQuestionAnswering",
      "MobileBertForSequenceClassification",
      "MobileBertForTokenClassification",
      "MobileBertLayer",
      "MobileBertModel",
      "MobileBertPreTrainedModel",
      "load_tf_weights_in_mobilebert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileBertForMaskedLM",
      "TFMobileBertForMultipleChoice",
      "TFMobileBertForNextSentencePrediction",
      "TFMobileBertForPreTraining",
      "TFMobileBertForQuestionAnswering",
      "TFMobileBertForSequenceClassification",
      "TFMobileBertForTokenClassification",
      "TFMobileBertMainLayer",
      "TFMobileBertModel",
      "TFMobileBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
